MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

MaligNet: Semisupervised Learning for Bone Lesion Instance Segmentation Using Bone Scintigraphy

One challenge in applying deep learning to medical imaging is the lack of labeled data. Although large amounts of clinical data are available, acquiring labeled image data is difficult, especially for bone scintigraphy (i.e., 2D bone imaging) images. Bone scintigraphy images are generally noisy, and...

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Bibliographic Details
Main Authors: Terapap Apiparakoon, Nutthaphol Rakratchatakul, Maythinee Chantadisai, Usanee Vutrapongwatana, Kanaungnit Kingpetch, Sasitorn Sirisalipoch, Yothin Rakvongthai, Tawatchai Chaiwatanarat, Ekapol Chuangsuwanich
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Bone scintigraphy
semi-supervised learning
lesion instance segmentation
deep learning
Online Access:https://ieeexplore.ieee.org/document/8979392/

Internet

https://ieeexplore.ieee.org/document/8979392/

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